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Heritability and genomics of gene expression in peripheral blood

Nature Genetics volume 46, pages 430–437 (2014)Cite this article

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Abstract

We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function or ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which we further examined in a replication set of 1,895 unrelated subjects. A large number of non-redundant local eQTLs (6,756) met replication criteria, whereas a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide a new resource toward understanding the genetic control of transcription.

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Figure 1: Transcriptome-wide estimates of heritability based on 2,752 twins.
Figure 2: Gene density and other predictors of heritability, using 2,616 paired twins and 18,392 genes.
Figure 3: Apparent heritability and local IBD effects versus true underlying distributions.
Figure 4: Comparison and replication of eQTL results.
Figure 5: Properties of distant eQTLs.

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Acknowledgements

The work described in this paper was funded by the US National Institute of Mental Health (RC2 MH089951, principal investigator P.F.S.) as part of the American Recovery and Reinvestment Act of 2009. Transport, extraction and preparation of the NTR samples were carried out under a supplement to the NIMH Center for Collaborative Genomics Research on Mental Disorders (U24 MH068457, principal investigator J.T.). We thank T. Lehner (National Institute of Mental Health) for his support. Additional analytic support was provided by grants R01 MH090936, R01 GM074175 and P42 ES005948 and by a Gillings Innovations Award. The Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR) were funded by the Netherlands Organization for Scientific Research (MagW/ZonMW; grants 904-61-090, 985-10-002, 904-61-193, 480-04-004, 400-05-717 and 912-100-20; Spinozapremie 56-464-14192; and Geestkracht program grant 10-000-1002), the Center for Medical Systems Biology (CMSB2; NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), the VU University EMGO+ Institute for Health and Care Research and the Neuroscience Campus Amsterdam, NBIC/BioAssist/RK (2008.024), the European Science Foundation (EU/QLRT-2001-01254), the European Community's Seventh Framework Programme (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413) and the European Research Council (ERC; 230374).

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Author notes

  1. Fred A Wright and Patrick F Sullivan: These authors contributed equally to this work.

Authors and Affiliations

  1. Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA

    Fred A Wright, Yi-Hui Zhou & Jung-Ying Tzeng

  2. Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA

    Fred A Wright, Yi-Hui Zhou & Jung-Ying Tzeng

  3. Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA

    Fred A Wright

  4. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Patrick F Sullivan, Paul Gallins & Laura Todd

  5. Department of Genetics, Rutgers University, New Brunswick, New Jersey, USA

    Andrew I Brooks & Jay Tischfield

  6. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Fei Zou, Wei Sun, Kai Xia, Vered Madar, Wonil Chung, Guanhua Chen, Ting-Huei Chen, Min Jin Ha & Zhaoyu Yin

  7. Department of Psychiatry, VU Medical Center, Amsterdam, The Netherlands

    Rick Jansen, Gerard van Grootheest, Johannes H Smit & Brenda W J H Penninx

  8. Department of Biological Psychology, VU University, Amsterdam, The Netherlands

    Abdel Abdellaoui, Jouke Jan Hottenga, Mathijs Kattenberg, Christel M Middeldorp, Jacqueline M Vink, Gonneke Willemsen, Eco J de Geus & Dorret I Boomsma

  9. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Sandra Batista, Casey Butler, Shunping Huang & Weibo Wang

  10. Environmental and Occupational Health Sciences Institute, Rutgers University, New Brunswick, New Jersey, USA

    David D'Ambrosio, Jaspreet Kochar, Ani Qu & Qi Wang

  11. Department of Pharmacotherapy and Outcomes Science, Virginia Commonwealth University, Richmond, Virginia, USA

    Andrey Shabalin

  12. Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA

    Wei Wang

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  1. Fred A Wright
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  2. Patrick F Sullivan
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  28. Jung-Ying Tzeng
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  30. Jacqueline M Vink
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  31. Qi Wang
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  32. Wei Wang
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  33. Weibo Wang
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  34. Gonneke Willemsen
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  39. Dorret I Boomsma
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Study design and writing: F.A.W., P.F.S., A.I.B., F.Z., W.S., B.W.J.H.P. and D.I.B. Analysis: F.A.W., P.F.S., F.Z., W.S., K.X., V.M., R.J., W.C., Y.-H.Z., A.A., G.C., T.-H.C., P.G., M.J.H., J.J.H., S.H., M.K., J.K., C.M.M., A.Q., A.S., J.-Y.T., Q.W., Wei Wang, Weibo Wang, G.W., J.H.S., E.J.d.G. and Z.Y. Genomic assays: A.I.B., D.D., J.T., A.Q. and Q.W. Phenotype collection: G.v.G. and J.M.V. Project management: L.T. Database design and management: S.B. and C.B.

Corresponding authors

Correspondence to Fred A Wright or Patrick F Sullivan.

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Competing interests

P.F.S. was on the scientific advisory board of Expression Analysis. The other authors report no conflicts of interest.

Integrated supplementary information

Supplementary Figure 1 Effects of covariates and mean expression on heritability and shared environmental effects (n = 2,752).

(a) Adjusted R2 for all covariates (not including hybridization plate) for predicting expression levels for each of 43,628 transcripts. (b) Heritability estimates (h2) from the ACE model applied to all transcripts, before and after covariate correction. Negative values are implausible, reflecting sampling variation, but the entire range is shown for illustration, and the estimates are unbiased. The correction generally strengthens the evidence for the most highly significant transcripts. (c) Shared environment (twinship) effect estimates c2, before and after covariate correction. (d) Covariate-corrected observed versus expected right-tailed P values for h2 show a large number of significant transcripts. (e) The same plot for positive c2 shows that none reaches transcriptome-wide significance, nor did any transcript show significant negative c2 evidence (data not shown).

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Supplementary Figure 2 Mean heritability estimates.

(a) Mean heritability as a function of gene proximity to 3,931 NHGRI GWAS catalog SNPs with GWAS P < 5.0 × 10−8. For each of the NHGRI catalog SNPs, the closest gene was recorded, then the second closest, and so on, and each gene was designated according to whether it was the kth-closest gene to at least one SNP in the catalog. The mean heritability as a function of these ranks shows that genes with higher proximity rank tend to have higher h2. A simple rank correlation of the two axis values gives P = 0.017. For each k, the number of genes included is ∼1,600. (b) Mean heritability as a function of the number of SNPs that are closest to the gene. For each gene, the number of significant NHGRI catalog SNPs for which the gene is closest was recorded, and the mean heritability was displayed for that group. Using all genes, the rank correlation of h2 versus the number of closest GWAS SNPs gives P = 8.2 × 10−16. For each point, the number of genes included in the calculation is shown.

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Supplementary Figure 3 Contribution of components to expression variation for 9,060 genes with h2 > 0.1 (whether or not declared expressed).

(a) Ratio of r2 (variation explained) by best local SNP to overall (twin-based) h2, with medians and means of ratios, as well as the proportion of h2 explained by the best local SNP. (b–d) Analogous plots and values for the best distant SNP (b), local GCTA estimation (c) and local IBD estimation (d) using DZ twins. Analyses using only expressed genes are similar throughout, due to the restriction h2 > 0.1.

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Supplementary Figure 4 Effects of sample size on the reliability of h2 estimates in twin-based designs and comparison with additive-only variance components, applied to 18,392 genes.

(a) From the shrunken ‘true” estimate of the h2 distribution in NTR, the distribution of estimated h2 was determined, using the twin proportions of the Brisbane Systems Genetics Study (BSGS) and assuming no family effects. These hypothetical h2 estimates from NTR (using the shrunken h2 distribution and computed error variation applicable to the BSGS sample size and analysis approach) very closely matches their published report. BSGS values were obtained by digitizing the plot from the authors' report using WebPlotDigitizer (http://arohatgi.info/WebPlotDigitizer/app/). (b) Using the shrunken 'true' estimate of the h2 distribution from NTR and the standard error of a twin-based design and ACE model (with MZ vs. DZ proportions the same as in NTR), the rank correlation of estimated h2 versus true h2 for the transcriptome as a function of total sample size.

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Supplementary Figure 5 Comparison of twin-based heritability to alternate sources of heritability information, using the 18,392 best h2 set of unique genes.

(a) Best local SNP r2 versus twin-based h2. The proportion variance in twin-based h2 explained by the best local SNP in a linear regression model is listed . Each gene was classified as ‘local’ or ‘distant’ according to the smaller of the eQTL P values (regardless of genome-wide significance). (b) Local GCTA r2 versus twin-based h2. (c) DZ local IBD analysis versus twin-based h2. The local IBD analysis is less powerful, as it is based on roughly half of the data. (d) A model using the three predictors in a–c produces only a slight improvement in prediction.

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Supplementary Figure 6 Reproducibility of GODOT eQTLs.

(a) Local eQTL –log10 (q) values for NESDA versus NTR. (b) Inset to highlight less significant local eQTLs. (c) Distant eQTL –log10 (q) values for NESDA versus NTR. The values shown are interchromosomal eQTLs, i.e., the SNP and target gene are on different chromosomes, ensuring that they are truly distant. (d) Inset to highlight less significant distant eQTLs.

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Supplementary Figure 7 Characteristics of local eQTLs.

The 6,864 SNPs involved in the 6,941 local eQTLs were annotated using Variant Effect Predictor (version 2.8) of Ensembl (see the Supplementary Note). (a) The proportion of local eQTLs located in regulatory regions. The number on the top of each bar is the P value indicating over-representation (red) and under-representation (blue) of the eQTLs located in regulatory regions. (b) The proportion of replicating local eQTLs.

Supplementary Figure 8 P values of SNPs in NTR and NESDA for genes declared significant in Westra et al.

(a) Local eQTL replication P values in NTR. (b) Local eQTL replication P values in NESDA. (c) Distant eQTL replication P values in NTR. (d) Distant eQTL replication P values in NESDA.

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Supplementary Figure 9 Properties of replicating distant eQTLs.

The 304 SNPs involved in the 348 distant eQTLs were annotated using Variant Effect Predictor (version 2.8) of Ensembl. Fourteen (26%) of 53 SNPs annotated as intergenic variants were replicated in NESDA, which is significantly lower than the overall replication rate of 47%. There was no significant enrichment or deficiency of replication in other categories.

Supplementary Figure 10 Distant eSNPs are more likely to be local eQTLs.

For each of the 304 SNPs of distant eQTLs, we assessed its association with local genes (> 1 Mb away), and we then grouped these 304 SNPs by their minimum local eQTL P values. The value above each black bar shows the number of SNPs belonging to each significance grouping. For comparison, we randomly selected 10,000 SNPs from the remaining SNPs, matched to the 304 SNPs by minor allele frequency and imputation quality R2, and then grouped analogously by their minimum local eQTL P values.

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Supplementary Figure 11 The distribution of 348 distant eQTLs.

A total of 304 SNPs were involved in the 348 distant eQTLs. The SNPs were clustered by genomic position so that a SNP was assigned to a cluster if its distance to any SNP already in the cluster was smaller than 1 Mb. The SNPs grouped into 203 clusters, 160 clusters with only a single SNP. Forty-three clusters with more than one SNP per cluster spanned 2 kb to 2 Mb, with median size of 89 kb. The plot shows the number of eQTLs belonging to each cluster. Eleven clusters with more than five eQTLs are highlighted in the inset table. The number of NESDA eQTLs (q value > 0.01) associated with each cluster is also shown.

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Supplementary Figure 12 Distant eQTLs and their associated genes on eight chromosomes.

(a) A distant eQTL on chromosome 19 is associated with the expression of 12 distant genes and a local gene MOY1F. The network plot shows the partial correlation graph of these 13 genes, where an edge indicating a nonzero partial correlation. The partial correlations were estimated by the penalized estimation method cited in the Supplementary Note. (b) A distant eQTL on chromosome 20 is associated with the expression of six distant genes and a local gene, SMOX. Using a likelihood ratio test approach and independent genotype and gene expression data from NESDA (to avoid winner's curse), we found that causal relations, eSNP → SMOX → a distant eQTL gene, are significantly more likely than other possible relations for these six distant eQTL genes, using the likelihood approach described in the Supplementary Note. (c) Distant eQTLs on six other chromosomes.

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Supplementary Figures 1–12, Supplementary Tables 1–7 and Supplementary Note (PDF 5924 kb)

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Wright, F., Sullivan, P., Brooks, A. et al. Heritability and genomics of gene expression in peripheral blood. Nat Genet 46, 430–437 (2014). https://doi.org/10.1038/ng.2951

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